Material classification is an important task in computer vision, as it provides important information for scene understanding. The segmentation of object surfaces according to their intrinsic composition has much potential for both lower-level inspection processes and higher-level object recognition. In particular, electrically conducting materials such as metal, and, dielectric (non-conducting) are two broad material classes with properties relevant to a number of applications. For example, a circuit board inspection problem requires the determination and verification of the placement of metal and insulator parts. In addition, the determination of material type can augment the geometrical shape description of objects to include intrinsic physical characteristics as well.
Some previous work (Glenn Healey and W. E. Blanz. Personal Communication.) on discriminating between dielectric/metal material types has consisted of training a group of known samples with respect to a set of features, an approach commonly used in conventional pattern recognition techniques. Such methods however have no definitive physical connection between discrimination and the intrinsic composition of the material. There has been some speculation about whether a definitive relationship exists between a set of features for the reflected color distribution and the intrinsic composition of the surface (G. Healey and T. O. Binford. Predicting material classes. In Proceedings of the DARPA Image Understanding Workshop, pages 1140-1146, Cambridge, Mass., April 1988); (G. Healey and W. E. Blanz. Identifying metal surfaces in color images. In SPIE Proceedings of Optics, Electro-Optics, and Sensors, Orlando, Fla., April 1988). In (G. Healey. Using color for geometry-insensitive segmentation. Journal of the Optical Society of America A, 6(6):920-937, June 1989), Healey proposes a method for distinguishing metals and inhomogeneous dielectric surfaces using the dichromatic reflectance model (S. Shafer. Using color to separate reflection components. Color Research and Application, 10:210-218, 1985); (G. Klinker, S. Shafer and T. Kanade. Using a color reflection model to separate highlights from object color. In Proceedings of the IEEE First International Conference on Computer Vision (ICCV), pages 145-150, London, England, June 1987).
A polarization-based method (L. B. Wolff. Polarization-based material classification from specular reflection. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 12(11):1059-1071, November 1990) was introduced for discriminating between dielectric and metal surfaces based upon Fresnel reflection theory. According to this theory, incident unpolarized light becomes strongly partially polarized upon specular reflection from dielectric surfaces over a large range of specular incident angles, while for metal surfaces such reflection is not as nearly partially polarized. Assuming that the specular component of reflection is significantly higher than the diffuse component, the ratio of the maximum to the minimum transmitted radiance sensed at a pixel, with respect to the rotation of a polarizer, can be used to estimate the polarization Fresnel ratio, a quantity that differentiates materials with respect to electrical conductivity. Segmentation of dielectric and metal can be achieved by thresholding this ratio of maximum to minimum transmitted radiance measured at each pixel, the threshold values being directly connected to physical material characteristics. This has been successfully implemented for circuit board inspection, identification of rust on metal, and identification of metal and dielectric parts of a number of objects.
A disadvantage of this polarization-based method is that it becomes inaccurate when the diffuse component of reflection is not small compared with the specular component. For instance, this method will misclassify highly diffuse white paper as metal. This disadvantage also sometimes constrains the use of an extended light source to cover more object points, since the diffuse component is boosted at the same time. Another limitation is that the specular incidence angle .psi. must be restricted to a certain range 30.degree.&lt;.psi.&lt;80.degree. for insured accuracy. Because this previous polarization-based method assumes incident unpolarized light, detection of metallic objects illuminated by the partially linear polarized light from clear or partly cloudy sky (e.g., for automatic target detection and recognition) has also been a problem.
The material classification method presented here, based upon the polarization phase of a light wave, is shown to have significantly distinct advantages in that this method functions quite well under almost all conditions which have been previously problematic to existing material classification techniques. The phenomenon of polarization phase is seen in varying states of elliptical polarization where the shape and orientation of the ellipse is determined by the relative phase difference between orthogonal light wave components (e.g., circular polarization results from orthogonal light waves being 1/4 wavelength relatively phase shifted). Polarization phase has been used before to aid in the determination of surface orientation for dielectric objects (K. Koshikawa and Y. Shirai. A model-based recognition of glossy objects using their polarimetric properties. Advances in Robotics, 2(2), 1987). The distinction between metal and dielectric with respect to phase of polarization is that metal retards light waves and therefore alters the phase of polarization of incident light upon specular reflection whereas dielectrics do not at all alter the phase of polarization of incident light. For instance, linearly polarized incident light will become elliptically polarized when reflected from a metal, but remain linearly polarized when reflected from a dielectric. The distinction between metal and dielectric material is therefore determined by whether there is a measurable retardance between the polarization phase of incident and reflected light with respect to an object. One way of measuring phase retardance is by measuring all four Stokes parameters of polarization such as what is accomplished in precise measurements for astronomy observations or by a measurement apparatus used in (K. Koshikawa and Y. Shirai. A model-based recognition of glossy objects using their polarimetric properties. Advances in Robotics, 2(2), 1987). Such an approach is usually unacceptable in a number of machine vision applications where either real-time response is required or the scene can move or change rapidly. An advance is presented whereby a much simpler methodology for measuring retardance without having to measure all four Stokes parameters is presented that is extremely sensitive to small shifts in phase. Furthermore, this methodology can be performed utilizing a modification to existing polarization sensing cameras (L. B. Wolff and T. A. Mancini. Liquid crystal polarization camera. In Proceedings of the IEEE Workshop on Applications of Computer Vision, pages 120-127, Palm Springs, Calif., December 1992) making it possible to implement this technique at near real-time rates.
The presented polarization phase based technique is applicable to scenes where incident illumination contains any non-zero magnitude component of linear polarization at any orientation except parallel or perpendicular to the plane of incidence. These illumination conditions are complementary to the existing polarization-based technique (L. B. Wolff. Polarization-based material classification from specular reflection. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 12(11):1059-1071, November 1990) where incident illumination is unpolarized. A key advantage of this polarization phase-based technique is that it can be utilized to determine metal/dielectric objects illuminated outdoors by a clear or partly cloudy sky, as skylight is partially linear polarized according to Rayleigh's Law (M. Born and E. Wolf. Principles of Optics. Pergamon Press, 1959). Combined with the existing polarization-based method which can detect metal/dielectric objects under cloudy (unpolarized) skies these methods can provide important information for automatic target detection and recognition algorithms under almost any sky condition. Another key advantage of the presented polarization-based technique is that even if specular reflection from a conductive surface is accompanied by a much larger diffuse reflection component, the measurement of retardance is limited only by the signal-to-noise ratio of the camera sensor being larger than the ratio of diffuse to specular reflection. This technique is so sensitive to the retardance of the linear polarized component, that phase shifting can be detected for most metals close to normal incidence, and close to grazing. In the laboratory where lighting is easily controlled, material classification is seen to perform very accurately using this technique.